Post-Training Network Compression for 3D Medical Image Segmentation: Reducing Computational Efforts via Tucker Decomposition.
- DOI
- 10.1148/ryai.240353
- Published
- 2025 Mar
- Container
- Radiology. Artificial intelligence
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1148/ryai.240353,
title = {Post-Training Network Compression for 3D Medical Image Segmentation: Reducing Computational Efforts via Tucker Decomposition.},
author = {Weber T and Dexl J and Rügamer D and Ingrisch M},
year = {2025},
journal = {Radiology. Artificial intelligence},
doi = {10.1148/ryai.240353},
url = {https://doi.org/10.1148/ryai.240353}
}RIS
TY - JOUR TI - Post-Training Network Compression for 3D Medical Image Segmentation: Reducing Computational Efforts via Tucker Decomposition. AU - Weber T AU - Dexl J AU - Rügamer D AU - Ingrisch M PY - 2025 JO - Radiology. Artificial intelligence DO - 10.1148/ryai.240353 UR - https://doi.org/10.1148/ryai.240353 ER -
APA
T, W., J, D., D, R., & M, I. (2025). Post-Training Network Compression for 3D Medical Image Segmentation: Reducing Computational Efforts via Tucker Decomposition.. Radiology. Artificial intelligence. https://doi.org/10.1148/ryai.240353
Source records
- pubmed · retrieved 2026-11-10T08:03:29.171Z